Fix optimizer beta type error for PyTorch 2.x+

[Mr-Neutr0n]

Summary

• Fix ValueError: betas must be either both floats or both Tensors when using PyTorch 2.x+
• Change 0** to 0.0** in beta calculations for both the generator and discriminator optimizers in setup_optimizers()
• The expression 0**net_g_reg_ratio (and 0**net_d_reg_ratio) produces an integer 0, but PyTorch 2.x Adam requires betas to be floats. Using 0.0** ensures a float result.

Details

In gfpgan/models/gfpgan_model.py, the setup_optimizers() method computes optimizer betas as:

betas = (0**net_g_reg_ratio, 0.99**net_g_reg_ratio)

Since 0 is an int and net_g_reg_ratio is also an int (value 1), 0**1 evaluates to int(0). Meanwhile 0.99**1 evaluates to float(0.99). This produces a mixed-type tuple (int, float) which PyTorch 2.x rejects with:

ValueError: betas must be either both floats or both Tensors

The fix changes the literal from 0 to 0.0 so that the exponentiation always produces a float, for both the generator optimizer (line 164) and the discriminator optimizer (line 179).

Fixes #638

Test plan

• Verify the change is minimal and correct: 0.0**x returns float for any numeric x
• Training with PyTorch 2.x+ no longer raises ValueError at optimizer construction